GP models provide a probabilistic approximation to an unknown function \(f(\theta )\) . Specifically, \(f(\theta )\)  is assumed to take the form of a GP, where each realization (or sample path) is a function. This prior assumption on the function can then be combined with the probability of actual observations conditional on the prior (a.k.a, the likelihood), using Bayes' rule [1]}. In this section, we provide a brief overview of the theory behind GP models and highlight the difference between exact and approximate inference, the latter finding applications in the presence of large datasets.
